Implications of prosthesis funding structures on the use of prostheses
Bibliographic record
Abstract
BACKGROUND: While sparsely researched, funding structures may play an important role in use of and satisfaction with prostheses and related health services. OBJECTIVES: The objectives of this study were to (1) quantify the direct costs of prosthesis wear, (2) explore variations in funding distribution, and (3) describe the role of affordability in prosthesis selection and wear. STUDY DESIGN: An anonymous, online cross-sectional descriptive survey was administered. METHODS: Analyses were conducted of qualitative and quantitative data extracted from an international sample of 242 individuals with upper limb absence. RESULTS: Access to prosthesis funding was variable and fluctuated with age, level of limb absence and country of care. Of individuals who gave details on prosthetic costs, 63% (n = 69) were fully reimbursed for their prosthetic expenses, while 37% (n = 40) were financially disadvantaged by the cost of components (mean [SD] US$9,574 [$9,986]) and their ongoing maintenance (US$1,936 [$3,179]). Of the 71 non-wearers in this study, 48% considered cost an influential factor in their decision not to adopt prosthesis use. CONCLUSIONS: Prosthesis funding is neither homogeneous nor transparent and can be influential in both the selection and use of a prosthetic device. CLINICAL RELEVANCE: Inequitable access to prosthesis funding is evident in industrialized nations and may lead to prosthesis abandonment and/or diminished quality of life for individuals with upper limb absences. Increased efforts are required to ensure equitable access to upper limb prosthetics and related services in line with individuals' needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".